Gapminder-R-Analysis

Gapminder Data Analysis

A comprehensive R-based project that analyzes the Gapminder dataset (1952–2007) to explore global trends in life expectancy, GDP per capita, and population. The analysis produces a suite of 12 visualizations, fits a regression model, and renders an HTML report.

Table of Contents

1. Project Overview
2. Features & Charts
3. Prerequisites
4. Installation
5. Usage
6. File Structure
7. Scripts & Automation
8. Docker & Containerization
9. Extending & Customizing
10. Data Source & Citations
11. License

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Project Overview

This repository contains everything needed to:

• Fetch and prepare the Gapminder dataset via the gapminder R package.
• Compute key metrics (global averages, per-country snapshots).
• Visualize 12 charts illustrating temporal trends, economic relationships, continental comparisons, and population impacts.
• Model the relationship between life expectancy and GDP per capita.
• Automate the workflow via shell scripts, a Makefile, and Docker.
• Render a polished RMarkdown report (Gapminder_report.html).

Ideal for students, educators, data scientists, and anyone interested in global development metrics.

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Features & Charts

1. Global Average Life Expectancy Over Time
2. Global Average GDP per Capita Over Time
3. Global Total Population Over Time
4. Scatter: Life Expectancy vs. GDP per Capita (2007)
5. Regression: Life Expectancy ~ log(GDP per Capita)
6. Top 10 Countries by Life Expectancy (2007)
7. Boxplot: Life Expectancy by Continent (2007)
8. Violin: GDP per Capita by Continent (2007)
9. Line: Average Life Expectancy by Continent Over Time
10. Density: Life Expectancy Distribution by Continent (2007)
11. Heatmap: Average Life Expectancy by Year & Continent
12. Bubble Plot: GDP vs. Life Expectancy Sized by Population (2007)

Each plot is saved to gap-<index>.png and displayed in the R session.

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Prerequisites

• R ≥ 4.0
• RStudio (optional, but recommended)
• Internet connection (to install R packages and fetch data)

Required R Packages

• ggplot2
• dplyr
• gapminder
• scales
• viridis
• tidyr
• forcats
• zoo
• rmarkdown (for report rendering)

The main script auto-installs any missing packages.

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Installation

1. Clone this repository:

   git clone https://github.com/yourusername/gapminder-analysis.git
   cd gapminder-analysis
   

2. Install R (if not already).
3. (Optional) Copy .env.example to .env to customize environment variables.

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Usage

Run Analysis & Save Plots

bash scripts/run_gapminder.sh

All 12 charts will print and save as gap-1.png … gap-12.png.

Render HTML Report

bash scripts/render_report.sh

This produces Gapminder_report.html.

Combined via Make

make report

Equivalent to running analysis and report steps.

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File Structure

.
├── Gapminder_Analysis.R       # Main R script
├── Gapminder_Analysis.Rmd     # RMarkdown report
├── Gapminder_report.html      # Generated HTML report
├── gap-*.png                  # Saved plot images
├── scripts/
│   ├── run_gapminder.sh       # Runs the analysis script
│   └── render_report.sh       # Renders the RMarkdown
├── Makefile                   # Make targets for automation
├── Dockerfile                 # Container definition
├── docker-compose.yml         # Compose for containerized run
├── .env.example               # Sample environment config
└── README.md                  # This file

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Scripts & Automation

• scripts/run_gapminder.sh Runs Gapminder_Analysis.R to produce and save all plots.

• scripts/render_report.sh Renders Gapminder_Analysis.Rmd to Gapminder_report.html.

• Makefile

  • make analysis — run shell script to generate plots
  • make report — run analysis + render report
  • make clean — delete images & report

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Docker & Containerization

Build and run in a container:

docker-compose up --build

This will:

1. Install system and R dependencies
2. Run the analysis script
3. Render the RMarkdown report

All files are shared via a bind mount.

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Extending & Customizing

• Add more plots: insert new code chunks in Gapminder_Analysis.R or .Rmd.
• Parameterize: use YAML or command-line args to filter years or continents.
• CI/CD: integrate with GitHub Actions to auto-build the report on push.
• Data sources: replace Gapminder with another tidy dataset for similar workflows.

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Data Source & Citations

• Gapminder dataset via R package:

  install.packages("gapminder")
  

• R Packages: see CRAN for ggplot2, dplyr, etc.

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License

This project is licensed under the MIT License. See LICENSE for details.

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